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Despite touting the value of green practices, many firms struggle to respond appropriately to the diverse environmental issues. The purpose of this paper is to investigate how the external and internal pressures interplay to influence top management championship, which, in turn, fosters the company’s green culture and the adoption of green practices. It thus helps to explain Chinese firms’ diversity with respect to the adoption of green practices.

A conceptual model is developed that summarizes the interplay of external and internal pressures, top management championship, green culture and the adoption of green practices. Data from 148 Chinese manufacturing firms were collected and a structural equation model was used for statistical analysis.

Government policy that provides incentives to adopt green practices and overseas customers’ green demand has significant positive influences on top management championship, while resources pressure has a significant negative effect. Government command and control policy, domestic customers’ green demand and organizational inertia do not impact top management championship. Furthermore, top management championship is positively correlated to both green culture and green practices, and green culture contributes to implementing green practices.

The findings help us understand which external and internal factors inspire or force top management to adopt green practices, and how they do so. Moreover, managers must also be aware of the bridging role of green culture. The findings will be valuable to policy makers in forming and enforcing “stick” or “carrot” environmental policies.

Leveraging a multi-theoretic approach, the authors’ research builds on insights from the institutional theory, natural resource-based view (NRBV) and upper echelons perspective, so as to increase the authors’ understanding on how firms adopt green practices to respond to environmental sustainability pressures. The institutional theory and the NRBV are leveraged in this study to recognize that firms perceive not only external institutional pressure for environmental management but also the internal pressure from resource constraints and capability to change. Upper echelons perspective is integrated into this study to explain the leadership role that top management serves in the management of the organization’s response to dynamic changes in the institutional environment and cultivate green culture within organization.

The purpose of this paper is to propose a risk-sharing model to coordinate the decision-making behavior of players in a cassava-based bioethanol supply chain under random yield and demand environment, so as to mitigate the yield and demand uncertainty risk and improve the bioethanol supply chain resiliency and performance.

The decision-making behavior under three models, namely, centralized model, decentralized model and risk-sharing model, are analyzed. An empirical test of the advantages and feasibility of the proposed risk-sharing model, as well as the test of yield uncertainty risk, risk-sharing coefficients and randomly fluctuating cassava market price on the decision-making behavior and performances are provided.

Though the proposed risk-sharing model cannot achieve the supply chain performance in the centralized model, it does help to encourage the farmers and the company to increase the supply of cassava and achieve the Pareto improvement of both players compared to the decentralized model. In particular, these improvements will be enlarged as the yield uncertainty risk is higher.

The findings will help decision makers in the bioethanol supply chain to understand how to mitigate the yield uncertainty risk and improve the supply chain resiliency under yield and demand uncertainty environment. It will also be conducive to ensure the supply of feedstock and the development of the bioethanol industry.

The proposed risk-sharing model incorporates the yield uncertainty risk, the random market demand and the hierarchical decision-making behavior structure of the bioethanol supply chain in the model.

Financing the capital-constrained farmers to facilitate the production of agri-products is one of the greatest challenges facing the farming supply chain in the developing countries. In this study, we investigate the optimal financing scheme for the farming supply chain under random yield and investment information asymmetry environment to support rural economic development.

We analyze a stylized model of farming supply chain where the capital-constrained farmer produces and sells agri-products through the agribusiness firm, and investigate the optimal financing scheme incorporating the investment information asymmetry and the challenge of yield uncertainty.

The results show that there is no one financing scheme equilibrium dominates for all situations, the financing scheme equilibrium is affected by the bank’s supervision cost to monitor the farmer’s moral hazard behavior, the variance of random yield and the farmer’s initial capital. The preference of the financing scheme for the agribusiness firm may be different from that for the farmer. The agribusiness firm might suffer from overfinancing problem under trade credit financing when the bank’s supervision cost is larger and the farmer’s own initial capital is lower; the higher variance of random yield will flare up the effect.

This study sheds light on the choice of financing scheme under random yield and investment information asymmetry environment. This problem is particularly important for developing economies. Financing the capital-constrained farmers not only increases supplies of food and industrial raw materials, but also reduces poverty. The findings provide managerial implications for practitioners for how to leverage different financing scheme to support rural economic development.

This study develops new theoretical model for farming supply chain financing incorporating the challenge of yield uncertainty and investment information asymmetry, the two prominent factors that would impact the financial risk significantly. We analyze the equilibrium under both bank financing and trade credit financing schemes, and the results suggest that the financing scheme equilibrium is affected by the bank’s supervision cost to monitor the farmer’s moral hazard behavior, the variance of random yield and the farmer’s initial capital. The agribusiness firm might suffer from overfinancing problem under trade credit financing.

While the usage of digital technology can bring many operational improvements for firms, it is unclear whether it can effectively improve firm resilience to deal with supply chain disruptions caused by emergencies such as COVID-19. From a dynamic capability perspective, this study aims to investigate how digital technology usage can improve firm resilience in a rapidly changing and turbulent environment.

Based on the survey sample of 237 Chinese firms, the stepwise regression approach was used to examine the proposed research hypotheses.

The empirical evidence shows that digital technology usage has a U-shaped effect on firm resilience, and that effect is fully achieved by first affecting market acuity and then promoting resource reconfiguration. Moreover, the authors further found that the U-shaped association between digital technology usage and firm resilience is derived from the U-shaped association between digital technology usage and market acuity.

This study enriches the resilience literature by revealing the mechanism of digital technology usage’s effects rather than focusing on the role of specific digital technologies. This study also provides guidance for firms to develop effective digital technology usage strategies.

This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability.

A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed.

Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above.

This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability.

The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data.

Roofing is one of the most dangerous jobs in the construction industry. Due to factors such as lack of planning, training and use of precaution, roofing contractors and workers continuously violate the fall protection standards enforced by the US Occupational Safety and Health Administration. A preferable way to alleviate this situation is automating the process of non-compliance checking of safety standards through measurements conducted in site daily accumulated videos and photos. As a key component, the purpose of this paper is to devise a method to detect roofs in site images that is indispensable for such automation process.

This method represents roof objects through image segmentation and visual feature extraction. The visual features include colour, texture, compactness, contrast and the presence of roof corner. A classification algorithm is selected to use the derived representation for statistical learning and detection.

The experiments led to detection accuracy of 97.50 per cent, with over 15 per cent improvement in comparison to conventional classifiers, signifying the effectiveness of the proposed method.

This study did not test on images of roofs in the following conditions: roofs initially built without apparent appearance (e.g. structural roof framing completed and undergoing the sheathing process) and flat, barrel and dome roofs. From a standpoint of construction safety, while the present work is vital, coupling with semantic representation and analysis is still needed to allow for risk analysis of fall violations on roof sites.

This study is the first to address roof detection in site images. Its findings provide a basis to enable semantic representation of roof site objects of interests (e.g. co-existence and correlation among roof site, roofer, guardrail and personal fall arrest system) that is needed to automate the non-compliance checking of safety standards on roof sites.

The purpose of this paper is to examine the effects of social resources on promoting information sharing practice and, thereby, improving firm performance. In particular, the authors are interested in addressing the following research questions. First, can the development of social capital (expressed in three dimensions: cognitive capital, structural capital, and relational capital) promote the content and quality of supply chain information sharing? Second, what are the relationships among the three social capital dimensions in the context of information sharing? Third, what are the effects of shared information (content and quality) on firm performance?

A theoretical model and several research hypotheses, well-grounded in the western literature, are developed. Data from 272 manufacturers in China were collected to test the model and the hypotheses. Structural equation modeling was used for statistical analysis.

The statistical results reveal that each social capital dimension has different effects on information sharing and performance. Namely, relational capital and cognitive capital have significant positive influences on information sharing. Structural capital has no direct positive impact on information sharing, but it displays indirect affects through the other two social capital dimensions. Furthermore, both the content and quality of the shared information improve manufacturing efficiency and responsiveness performance. Finally, the paper also recognizes possible reciprocal causality between relational capital and cognitive capital.

First, considering the distinct role of social relations in China, future studies should examine the influence of social capital and the potential reciprocal relationship between trust and shared vision, using data from other countries. Second, data were collected solely from the Pearl River Delta, China. Studies based on samples drawn from other regions, such as the Yangtze River Delta, the Bohai Sea economic area, and southwest China, would provide a degree of geographic and economic diversity and extend the generalizability of the results.

Despite the touting of the value of information sharing, many companies struggle with the practice. The findings help us understand the process by which social capital accumulates and contributes to information sharing. Namely, firms must first engage in social interactions with supply chain partners in order to develop a trusting relationship and a shared vision for information sharing. The managers must also be aware of the possible reciprocal relationship between trust and shared vision. Both the volume and content of information sharing are critical to the performance.

Manufacturers can use the concept of social capital to build relational rents for information sharing.

Responding to the call from the literature, this study extends the discussion of antecedents and consequences of supply chain information sharing, with a focus on the influences of relational resources. The paper proves that social capital provides a valid theoretical base from which to examine the role of social relations in promoting supply chain information sharing. Previous supply chain research in social capital often limited its consideration of social capital to relational capital. Understanding the effects of all three dimensions of social capital and their inter-relationships would contribute to the process by which social capital accumulates and promotes information sharing. Additionally, a study with the Chinese data should validate the theoretical model developed based on western literature, and offer valuable insights to researchers and practitioners from both economic and cultural perspectives.

To obtain an understanding of the disposition of Chinese agriculture.

By applying econometric methods to make a narrow assessment on several productive factors in Chinese agriculture covering most of the era of Reforms and openness, a picture portraying the traits of Chinese rural society is provided. The author delves deep into the foundations of econometric as well as western society to draw comparisons between Occidental and oriental society.

Unlike the widely held view that implicitly identifies the basis of studies in Chinese economical development with that in western nations, the presented idea illuminates the intrinsic “upper‐hand” disposition of Chinese rural society, which has so far practically made China tread a path different from that in western society.

Since the paper deals with the whole picture of Chinese agriculture, it presumably may cause partial loss of accuracy in econometric calculations.

It provides a fresh yet in‐depth idea for western researchers.

The paper breaks fresh ground in Chinese study and economic theory for researchers who are confused with the intricacy of the Chinese agricultural economy.

The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of high-speed railway so as to provide a new way of thinking and method for the detection of contact wire injuries of high-speed railway.

Based on the principle of eddy current detection and the specification parameters of high-speed railway contact wires in China, a finite element model for eddy current testing of contact wires was established to explore the variation patterns of crack signal characteristics in numerical simulation. A crack detection system based on eddy current detection was built, and eddy current detection voltage data was obtained for cracks of different depths and widths. By analyzing the variation law of eddy current signals, characteristic parameters were obtained and a quantitative evaluation model for crack width and depth was established based on the back propagation (BP) neural network.

Numerical simulation and experimental detection of eddy current signal change rule is basically consistent, based on the law of the selected characteristics of the parameters in the BP neural network crack quantitative evaluation model also has a certain degree of effectiveness and reliability. BP neural network training results show that the classification accuracy for different widths and depths of the classification is 100 and 85.71%, respectively, and can be effectively realized on the high-speed railway contact line cracks of the quantitative evaluation classification.

This study establishes a new type of high-speed railway contact wire crack detection and identification method, which provides a new technical means for high-speed railway contact wire injury detection. The study of eddy current characteristic law and quantitative evaluation model for different cracks in contact line has important academic value and practical significance, and it has certain guiding significance for the detection technology of contact line in high-speed railway.